BSTS Predict

This function predicts the time series based on a trained BSTS model, i.e. a fitted "hanaml.BSTS" object.

# S3 method for BSTS
predict(model, data = NULL, key = NULL, exog = NULL, horizon = NULL)

Arguments

model: R6Class object
A "hanaml.BSTS" object for prediction.
data: DataFrame
Input DataFrame containing the time-series data for BSTS prediction.
key: character
The ID column that representing the order of time-series values in data.
exog: character or list of characters, optional
An optional array of exogenous variables.
horizon: integer, optional
Number of predictions for future observations.
Defaults to 1.

Value

Named list of DataFrames

result: DataFrame containing the forecast values and other related statistics(like standard error estimation, upper/lower quantiles).
components: DataFrame containing the trend/seasonal/regression components w.r.t. the forecast values.

Examples

Assuming bs is a fitted "hanaml.BSTS" object:


> data_pred
   TIME_STAMP  FEATURE_01  FEATURE_02  FEATURE_03  ...  FEATURE_07  FEATURE_08  FEATURE_09  FEATURE_10
0          50       0.471      -0.660      -0.086  ...      -1.107      -0.559      -1.404      -1.646
1          51       0.872       0.062       0.481  ...      -0.729       0.894      -0.754       1.107
2          52       0.976      -0.003       0.824  ...      -0.589       0.133       0.007      -0.115
3          53       0.446       0.231       0.098  ...      -0.014       0.182      -0.465      -1.062
4          54       0.248      -0.142       0.174  ...      -0.380       1.236      -0.552      -1.051
5          55      -0.319      -0.867       0.334  ...      -0.160      -0.488      -0.650      -0.769
6          56      -0.194      -0.822       0.523  ...      -0.566      -0.289      -0.596      -0.559
7          57      -0.357      -0.564      -0.391  ...      -0.980       0.578      -0.948      -0.870
8          58      -0.760      -1.113      -0.178  ...      -0.477      -0.705      -1.199      -0.517
9          59      -0.611      -1.163       0.186  ...      -0.976      -0.576      -0.927      -1.577

> res <- predict(bs, data_pred, key = 'TIME_STAMP')
> res[[1]]
   TIME_STAMP  FORECAST        SE  LOWER_80  UPPER_80  LOWER_95  UPPER_95
0          50  0.143151  0.591231 -0.614542  0.900844 -1.015640  1.301943
1          51  0.469405  0.765558 -0.511697  1.450508 -1.031060  1.969871
2          52  0.155813  1.004786 -1.131872  1.443499 -1.813531  2.125158
3          53  0.055188  1.160655 -1.432251  1.542627 -2.219653  2.330029
4          54  0.064481  1.385078 -1.710569  1.839531 -2.650222  2.779185
5          55  0.045844  1.660894 -2.082678  2.174365 -3.209448  3.301135
6          56 -0.039227  1.905115 -2.480732  2.402277 -3.773185  3.694731
7          57  0.124084  2.193157 -2.686560  2.934728 -4.174424  4.422592
8          58 -0.200588  2.479858 -3.378655  2.977478 -5.061020  4.659843
9          59  0.339182  2.763764 -3.202725  3.881089 -5.077696  5.756059

Arguments

Value

Examples

See also